Traffic light detection based on improved YOLOV5

Traffic light detection is a part of modern intelligent driving, which affects the driving safety of smart cars. However, the object of traffic lights is small and the actual road conditions are complex. The classic object detection algorithm cannot achieve a good detection effect on small signal lights. For this situation, This paper proposes an improved yolov5 model. Because the object of the traffic light is small, the feature extracted by the P2 layer with a small receptive field is introduced. Inspired by the MOAT model, a C3_MOAT structure is proposed. At the same time, a structural re-parameterized RepConv module is added to the Head part of YOLOV5 to improve the feature extraction of the model. ability. Through the above improvements, the ability of the model to detect signal light objects is improved. Experiments show that the improved algorithm improves the map@.5 and map@.5:.95 indicators in the Bosch dataset by 2.6% and 2.7% respectively, and in the S2TLD dataset map@.5 and map@.5:.95 Indicators were increased by 2.2% and 1.6%. Compared with the YOLOV5 network, it has a good improvement.